Methods and systems to diagnose depression

ABSTRACT

A method includes receiving sensor data at a processor from sensors of an external medical device. The sensor data corresponds to at least a first body parameter value for a patient and a second body parameter value for the patient. The method includes determining a first depression-indicative value based on the first body parameter value, a second depression-indicative value based on the second body parameter value, a depression detection value as a function of a first weight applied to the first depression-indicative value and a second weight applied to the second depression-indicative value, and a depression state based at least in part on a comparison of the depression detection value to one or more threshold values.

CROSS REFERENCE TO RELATED APPLICATION

This patent application is a continuation of U.S. patent applicationSer. No. 13/746,478, filed Jan. 22, 2013, which is incorporated hereinby reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure is generally related to cranial nerve stimulationto treat depression disorders.

BACKGROUND

The human nervous system includes the brain and the spinal cord,collectively known as the central nervous system. The central nervoussystem includes nerve fibers that transmit nerve signals to, from, andwithin the brain and spinal cord. The network of nerves in the remainingportions of the human body forms the peripheral nervous system. A systemof peripheral nerves connects directly to the brain to control variousbrain functions, such as vision, eye movement, hearing, facial movement,and feeling. Another system of peripheral nerves, known as the autonomicnervous system, controls autonomic functions. Autonomic functionsinclude blood pressure, body temperature, heartbeat, blood vesseldiameter control, intestinal movements, actions of many internal organs,and other body activities and functions that occur without voluntarycontrol.

Neurological disorders may affect the human nervous system. Someneurological disorders (e.g., epilepsy and depression) may be monitored,treated with medication, with neurostimulation, or a combinationthereof. Neurostimulation may include electrical stimulation of thenervous system. Forms of neurostimulation may include cranial nervestimulation, such as vagus nerve stimulation (VNS) or trigeminal nervestimulation (TNS). A device that applies TNS may be configured to applysubcutaneous TNS, transcutaneous TNS, or both. VNS and subcutaneous TNSmay require surgical implantation of electrodes in a patient.Transcutaneous TNS may be implemented by external electrodes coupled tothe patient in one or more regions where nerve branches are near thesurface of the skin.

SUMMARY

A medical device may be used to implement cranial nerve stimulation(CNS) to treat depression disorders (e.g., major depressive disorder,dysthymia, seasonal affective disorder, and postpartum depression). CNSmay include TNS, VNS, stimulation of other cranial nerves, or acombination thereof. The medical device may be configured to applyopen-loop therapy (e.g., a scheduled therapy or treatment) to a patient.The open-loop therapy may inhibit the occurrence of depression episodesin the patient, reduce duration of depression episodes that do occur,reduce intensity of depression episodes that do occur, or combinationsthereof. The medical device may also be configured to implementclosed-loop therapy (e.g., an episode therapy or treatment) whenprocessed sensor data indicates onset of a depression episode. Theclosed-loop therapy may limit depression episode duration, depressionepisode severity, or both. Open-loop therapy, closed-loop therapy, orboth, may include CNS, administration of medicine, other treatment, orcombinations thereof.

In a particular embodiment, a system includes a first sensor configuredto provide first sensor data corresponding to a first body parametervalue for a patient. The system also includes a second sensor configuredto provide second sensor data corresponding to a second body parametervalue for the patient. The system further includes a processing unitconfigured to receive the first sensor data and the second sensor data.The processing unit includes a processor configured to determine a firstdepression-indicative value based on the first body parameter value anda second-depression indicative value based on the second body parametervalue. The processor is also configured to determine a depressiondetection value as a function of a first weight applied to the firstdepression-indicative value and a second weight applied to the seconddepression-indicative value. The processor is further configured todetermine a depression state based at least in part on a comparison ofthe depression detection value to one or more threshold values.

In a particular embodiment, a method includes receiving sensor data at aprocessor from sensors of an external medical device. The sensor datacorresponds to at least a first body parameter value for a patient and asecond body parameter value for the patient. The method includesdetermining, via the processor, a first depression-indicative valuebased on the first body parameter value and a seconddepression-indicative value based on the second body parameter value.The method includes determining, via the processor, a depressiondetection value as a function of a first weight applied to the firstdepression-indicative value and a second weight applied to the seconddepression-indicative value. The method includes determining, via theprocessor, a depression state based at least in part on a comparison ofthe depression detection value to one or more threshold values. Themethod may also include automatically adjusting, via the processor, atleast one of the first weight and the second weight in response to anindication contradicting the depression state.

In a particular embodiment, a non-transitory computer-readable mediumincludes instructions executable by a processor. The instructions may beexecutable by the processor to receive sensor data from sensors of anexternal medical device. The sensor data corresponds to at least a firstbody parameter value for the patient and a second body parameter valuefor the patient. The instructions may be executable by the processor todetermine a first depression-indicative value based on the first bodyparameter value and a second depression-indicative value based on thesecond body parameter value. The instructions may be executable by theprocessor to determine a depression detection value as a function of afirst weight applied to the first depression-indicative value and asecond weight applied to the second depression-indicative value. Atleast a value for the first weight depends on one or more valuesdetermined from the sensor data. The instructions may also be executableby the processor to determine a depression state based at least in parton a comparison of the depression detection value to one or morethreshold values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an embodiment of a medical device systemthat uses cranial nerve stimulation (CNS) to treat a depressiondisorder.

FIG. 2 is a block diagram of an external medical device of a medicaldevice system that uses CNS signals to treat a depression disorder.

FIG. 3 is a flow chart of a first particular embodiment of a method ofuse of sensor data from a medical device that enables application of CNSsignals to treat a depression disorder.

FIG. 4 is a flow chart of a second particular embodiment of a method ofuse of sensor data from a medical device that enables application of CNSsignals to treat a depression disorder.

DETAILED DESCRIPTION

A medical device system may be used to provide cranial nerve stimulation(CNS) to a patient to treat a depression disorder. The CNS may includetrigeminal nerve stimulation, vagus nerve stimulation, stimulation ofother cranial nerves, or combinations thereof. The medical device systemmay enable the application of open-loop therapy to the patient. Theopen-loop therapy may reduce depression episode frequency, depressionepisode intensity, depression episode duration, or combinations thereof,for the patient. The medical device system may also enable theapplication of closed-loop therapy. Closed-loop therapy may be initiatedvia the medical device system when a depression state determined fromsensor data collected by the medical device system indicates onset of adepression episode. The closed-loop therapy may limit depression episodeduration, depression episode intensity, or both, for the patient.Open-loop therapy, closed-loop therapy, or both, may include CNSsignals, medicine delivery, other treatment, or combinations thereof.

The medical device system may include a medical device and a processingunit that are external to the patient. The medical device may includeone or more sensors and one or more pairs of electrodes that enableapplication of CNS (e.g., trigeminal nerve stimulation). The processingunit may initiate scheduled CNS signals at appropriate times. Theprocessing unit may also initiate closed-loop therapy when a depressionstate determined from collected sensor data indicates depression episodeonset. The processing unit may cease the closed-loop therapy when thedepression state determined from the collected sensor data indicatesdepression offset.

FIG. 1 is a representation of a particular embodiment of a medicaldevice system 100 that uses CNS to treat a patient 102 that has beendiagnosed as having a depression disorder (e.g., major depressivedisorder, dysthymia, seasonal affective disorder, and postpartumdepression). The medical device system 100 may include a medical device104 that attaches to the patient 102 and a processing unit 106. In anembodiment, the medical device 104 may include one or more adhesivepatches that attach to the patient 102. In other embodiments, themedical device 104 may be attached to an article of clothing (e.g., aheadband, scarf, hat, or other clothing item) that the patient 102wears. The medical device 104, the article of clothing, or both mayinclude one or more indicators that facilitate correct placement of themedical device 104 on the patient 102.

The medical device 104 may include one or more treatment devices,sensors, or combinations thereof for treating and monitoring the patient102. For example, the medical device 104 may include one or more pairsof electrodes that enable application of trigeminal nerve stimulation(TNS) and collection of skin conductance data. The medical device 104may also include other sensors such as, but not limited to, a skintemperature sensor, a heart rate sensor, an oximeter, an accelerometer,a pedometer, or combinations thereof.

The medical device 104 may communicate with the processing unit 106.Sensors of the medical device 104 may send sensor data to the processingunit 106. The medical device 104 may also receive CNS signals from theprocessing unit 106 that enable one or more pairs of electrodes of themedical device 104 to apply CNS to the patient 102.

Communications between the medical device 104 and the processing unit106 may include wireless communications, wired communications, orcombinations thereof. In an embodiment, the processing unit 106 and themedical device 104 are an integrated unit and communications between theprocessing unit 106 and the medical device 104 are accommodated byinternal connections in the integrated unit. In another embodiment, atleast a portion of the processing unit 106 and the medical device 104are an integrated unit and communications between the processing unit106 and the medical device 104 are accommodated by internal connectionsin the integrated unit. In another embodiment, at least a portion ofcommunications between the medical device 104 and the processing unit106 are accommodated via a wired connection 108. In another embodiment,at least a portion of communications between the medical device 104 andthe processing unit 106 are accommodated via wireless communications(e.g. by radio frequency communications, by infrared communications, byinternet communications, or combinations thereof).

The medical device 104, the processing unit 106, or both may include oneor more batteries, capacitors, other power supplies, or combinationsthereof, to power the treatment devices and the sensors of the medicaldevice 104 and to power the processing unit 106. Batteries for themedical device 104 and the processing unit 106 may be rechargeablebatteries, disposable batteries, or combinations thereof. The medicaldevice 104, the processing unit 106, or both may be coupled to anothertype of power supply (e.g., an electromagnetic signal to enableinductive powering or an electrical outlet) to provide operationalpower, power to recharge batteries, capacitive charging, or combinationsthereof.

The processing unit 106 may include a processor 110, a memory 112, andinterfaces 114, 116. The processor 110 may be a single processor of theprocessing unit 106 or multiple processors of the processing unit 106.The memory 112 may include instructions executable by the processor 110to operate the medical device system 100. The interfaces 114, 116 mayenable the processing unit 106 to communicate with other devices. One ormore device interfaces 114 may enable wired or wireless communicationdirectly with certain devices (e.g., the medical device 104, a sensor118, a treatment device 120, a second treatment device 130, and acomputer system 122). One or more network interfaces 116 may enable theprocessing unit 106 to communicate with certain devices (e.g., themedical device 104, the sensor 118, the treatment device 120, a secondtreatment device 130, and the computer system 122) via a network 124.The network 124 may be a local area network, the internet, atelecommunication network, other communication network, or combinationsthereof. For example, the processing unit 106 may send a treatmentinitiation signal from the interface 116 to a uniform resourceidentifier (URI) associated with the treatment device 120 via thenetwork 124.

The processing unit 106 may also include a display 126, one or moreinput devices 128 (e.g., input buttons, a touch pad, a keyboard, a keypad, etc.), one or more output devices (e.g., speakers, vibrationdevices, etc.), other components, or combinations thereof. In someembodiments, the display 126 may be an input device of the one or moreinput devices 128 (i.e., a touch screen). The display 126, the one ormore input devices 128, and the one or more output devices mayfacilitate communications and delivery of notifications between theprocessing unit 106, the patient 102, and healthcare provider personnel.

The processor 110 of the processing unit 106 may request sensor datafrom one or more sensors. Alternately, one or more sensors mayautomatically send sensor data to the processor 110. The processing unit106 may receive sensor data from the medical device 104 and from one ormore other sensors. For example, the processing unit 106 may receivesensor data from the medical device 104 and from the sensor 118. Thesensor 118 may be a respiration sensor, a sphygmomanometer (i.e., ablood pressure meter), a pedometer, another sensor, or combinationsthereof that provides sensor data to the processing unit 106 wirelesslyor by a wired connection. In an embodiment, the sensor 118 may becoupled to the processing unit 106, the patient 102, or both,substantially during a time period when the medical device 104 iscoupled to the processing unit 106 and the patient 102. In otherembodiments, the sensor 118 may be coupled to the processing unit 106,the patient 102, or both, for selected time periods. For example, thesensor 118 may be a sphygmomanometer that the patient 102 connects tothe processing unit 106 during waking hours and disconnects when thepatient 102 prepares to go to sleep. The sensor data may be received atthe processing unit 106 as sensor data sets associated with particulartimes.

The processor 110 of the processing unit 106 may process received sensordata (e.g., data received from the sensors of the medical device 104,the sensor 118, or both). Processing the sensor data may includedetermining one or more values from the sensor data, saving the sensordata in the memory 112, saving determined values in the memory 112,sending the sensor data to the computer system 122, sending determinedvalues to the computer system 122, or combinations thereof. The computersystem 122 may be one or more computational devices associated with ahealthcare provider of the patient 102. The determined values may bebody parameter values, depression-indicative values, depressiondetection values, depression states, other values, or combinationsthereof. Sensor data may be streamed to the computer system 122 or maybe sent to the computer system 122 at selected times.

The processing unit 106 may send open-loop therapy signals to initiateopen-loop therapy of the patient 102. The open-loop therapy signals maybe sent at regular intervals beginning at particular times or beginningwhen processed sensor data indicates that one or more body parametervalues are in particular ranges. The open-loop therapy may be CNSsignals (e.g., trigeminal nerve stimulation signals, vagus nervestimulation signals, or other stimulation signals for other cranialnerves), medicine delivery, or combinations thereof. The open-looptherapy may be determined based on one or more treatment regimens. Eachtreatment regimen may be associated with a set of treatment parameters.The treatment parameters may include, but are not limited to, treatmentintensity (e.g., signal amplitude or medicine dosage), stimulationfrequency (e.g., how often the open-loop therapy is to occur), pulsefrequency of CNS signals, duty cycle of CNS signals, charge balance ofCNS signals, stimulation location, stimulation direction, type ofstimulation (e.g., CNS signals, medicine, or both), or combinationsthereof. In some embodiments, medicine delivery may be instigated via amedicine pump. In other embodiments, the processing unit 106 may providea visual notification, an audio notification, a haptic notification(e.g., a vibration), or combinations thereof, to inform the patient 102that the patient should take medicine. Information (e.g., treatmentparameters) that enables the processing unit 106 to send the open-looptherapy signals may be received by the processing unit 106 as input fromthe healthcare provider via the one or more input devices 128, bytransmission from the computer system 122, or both. Appropriateauthentication, such as user identification and a password, may berequired to initiate open-loop therapy, change open-loop therapy, orstop open-loop therapy.

The open-loop therapy signals may include scheduled CNS signals toinhibit the occurrence of depression episodes in the patient, reduceduration of depression episodes that occur, reduce intensity ofdepression episodes that occur, or combinations thereof. The processingunit 106 may apply different scheduled CNS treatment regimens atdifferent times. For example, the processing unit 106 may apply a firstscheduled CNS treatment regimen of CNS signals during hours when thepatient 102 is typically awake, and may apply a second regimen ofscheduled CNS signals, or no CNS signals, during hours when the patient102 is typically asleep. Instead of, or in addition to, basing variousregimens on time, the processing unit 106 may base various regimens onprocessed sensor data. For example, one or more body parameter valuesfor the patient 102 may indicate that the patient is awake and theprocessing unit 106 may apply the first scheduled CNS treatment regimenduring such times. One or more of the body parameter values for thepatient 102 may change to values that indicate that the patient 102 isin or is approaching a sleep state. The processing unit 106 may applythe second regimen of scheduled CNS signals, or no CNS signals, when theone or more of the body parameter values indicate that the patient 102is in or is approaching the sleep state.

The processing unit 106 may enable application of closed-loop therapy inresponse to detection of depression onset. The depression onset may bedetermined by the processing unit 106 based on the sensor data.Alternately, the depression onset may be determined by the computersystem 122 based on the sensor data. Cessation of the closed-looptherapy may occur in response to depression offset, which may indicatethat a depression episode has substantially finished. The processingunit 106 may also be responsive to commands from the computer system 122to cease the closed-loop therapy, the open-loop therapy, or both.

The closed-loop therapy may include closed-loop therapy CNS signals, mayinclude treatment provided by one or more other treatment devices (e.g.,by the treatment device 120, the second treatment device 130), or acombination thereof. In an embodiment, the closed-loop therapy may beone or more replacement CNS signals that replace scheduled CNS signals.In another embodiment, the closed-loop therapy includes adjusting atleast one parameter of the scheduled CNS signals to be different than acorresponding parameter of the scheduled CNS signals. The parameters mayinclude, but are not limited to, amplitude, polarity, frequency, pulsewidth, pulse period, duty cycle, charge balancing, and signal duration.Replacement CNS signals or the modified scheduled CNS signals may bestronger than the scheduled CNS signals (e.g., current amplitude ofclosed-loop therapy CNS signals may be greater than current amplitude ofscheduled CNS signals).

In addition to, or in lieu of, sending closed-loop therapy CNS signalsto the patient 102, the processing unit 106 may send treatmentinitiation signals to one or more second treatment devices. For example,the processing unit 106 may send a treatment initiation signal to thetreatment device 120 and/or the second treatment device 130 when adetermined depression state indicates onset of a depression episode. Thetreatment initiation signal may cause the treatment device 120 and/orthe second treatment device 130 to provide closed-loop therapy to limitdepression episode duration, depression episode severity, or both. Thetreatment device 120 may be a controller of a vagus nerve stimulation(VNS) system, a medicine delivery system, another device, orcombinations thereof. When the treatment device 120 is the controller ofthe VNS system, the controller may instruct the VNS system to applyclosed-loop therapy VNS signals to the patient 102. The VNS system maybe surgically implanted in the patient 102. To extend the working lifeof the VNS system, to inhibit the patient from becoming accustomed toVNS treatment, for other reasons, or for combinations thereof, the VNSsystem may be used in conjunction with CNS provided by the medicaldevice 104 to the patient 102. When the treatment device 120 is themedicine delivery system (e.g., a medicine pump), the treatment device120 may deliver a dose or doses of medicine to the patient 102 inresponse to the treatment initiation signals. When the treatment device120 is the medicine delivery system, the treatment device 120, theprocessing unit 106, or both may include safeguards that limit theamount of medicine that can be delivered to the patient 102 in aparticular time period. The second treatment device 130 may be anexternal vagus nerve stimulation (VNS) device configured to externallycouple to the auricular branch of the patient's vagus nerve located atthe patient's ear.

The processing unit 106 may be operational in different modes. A firstmode may enable the processing unit 106 to collect sensor data from thepatient 102 without application of treatment to the patient 102. Thesensor data may be used to determine initial values of variables used todetermine a depression state of the patient 102. A second mode mayenable the processing unit 106 to collect sensor data from the patient102 and to apply open-loop therapy to the patient 102. A third mode mayenable the processing unit 106 to collect sensor data from the patient102, to apply open-loop therapy to the patient 102, and to applyclosed-loop therapy to the patient 102 when a depression statedetermined from the data indicates onset of a depression episode. Afourth mode may enable the processing unit 106 to collect data from thepatient 102 and to apply closed-loop therapy to the patient 102 when adepression state determined from the data indicates onset of adepression episode without applying open-loop therapy to the patient102. The processing unit 106 may have fewer operational modes,additional operational modes, or different operational modes. Ahealthcare provider may be able to change the operational mode of theprocessing unit 106 via the one or more input devices 128, viatransmission of a mode change instruction to the processing unit 106, orboth. Appropriate authentication, such as user identification and apassword, may be required to change the operational mode.

In an embodiment, the patient 102 may receive the medical device system100 from the healthcare provider. The healthcare provider may providethe patient 102 with instructions on proper placement of the medicaldevice 104, attachment of sensors (e.g., the medical device 104 and thesensor 118) to the processing unit 106 and the patient 102, useroperation and maintenance of the processing unit 106, and otherinformation pertaining to the medical device system 100. The processingunit 106 may be preprogrammed with instructions and data to enableoperation of the medical device system 100.

The patient 102 may put on the medical device 104. The patient mayconnect the medical device 104 to the processing unit 106 and activatethe medical device system 100. When the medical device system 100 isactivated, diagnostic instructions in the memory 112 of the treatmentdevice 120 may be executed by the processor 110 to ensure that theprocessing unit 106 is operating properly. At selected times, theprocessor 110 may execute the diagnostic instructions during operationof the medical device system 100 to ensure that the medical devicesystem 100 continues to operate properly. Should the diagnosticinstructions indicate a problem, the instructions may cause theprocessing unit 106 to generate a notification to the patient 102, thehealthcare provider, or both. The notification may include audio output,visual output, haptic output (e.g., vibration of the processing unit106), or combinations thereof. In an embodiment, the notification mayinclude information presented via the display 126 of the processing unit106 that identifies the problem. For example, the display 126 may showthe statement “Please connect the medical device to the processing unit”if execution of the diagnostic instructions indicate no communicationwith the medical device 104. Depending on the detected problem, themedical device system 100 may also perform other actions, such asinhibiting application of treatment to the patient 102. When executionof the diagnostic instructions do not indicate problems with the medicaldevice system 100, the medical device system 100 may collect sensordata, begin application of open-loop therapy to the patient 102, enableclosed-loop therapy upon detection of a depression state that indicatesonset of a depression episode, or combinations thereof depending on anoperational mode of the processing unit 106.

The medical device 104 depicted in FIG. 1 is positioned above the eyesto enable stimulation of supraorbital branches of the trigeminal nerve.In other embodiments, the medical device 104 or portions of the medicaldevice 104 may be configured to be positioned at another region of theface to stimulate other portions of the trigeminal nerve or anothercranial nerve. For example, the medical device 104 may be, or mayinclude, one or more patches that are configured to be placed near thenose to allow for stimulation of infraorbital branches of the trigeminalnerve.

A computation processor may be used to process sensor data received fromsensors coupled to the processing unit 106 (e.g., the sensors of themedical device 104 and the sensor 118) to determine a depression stateof the patient 102. The computational processor may be the processor 110of the processing unit 106, may be a processor or processors of thecomputer system 122, may be a processor or processors of another device,or combinations thereof. The processing unit 106 may send the sensordata to the computation processor as raw sensor data, processed sensordata, or both. For example, the processing unit 106 may receive skinconductivity data from one or more skin conductivity sensors of themedical device 104, and the processing unit 106 may send the receivedskin conductivity data to the computation processor without processingthe data. In another example, the processing unit 106 may receiveoximeter data from an oximeter of the medical device 104, and theprocessor 110 of the processing unit 106 may calculate a heart rateand/or blood oxygen saturation from the oximeter data. The processingunit 106 may send the heart rate, the blood oxygen saturation, or bothto the computation processor instead of, or along with, the oximeterdata.

The sensor data may be received by the computation processor in sensordata sets. Each sensor data set may be associated with a particular time(t). A sensor data set may be used to determine a depression state forthe patient 102 at the particular time. Sensor data of a sensor data setmay correspond to at least one body parameter value (PV(t)) for thepatient 102. For example, sensor data of a sensor data set may includetemperature data from a temperature sensor. The temperature data maycorrespond to skin temperature for the time associated with the sensordata set. Also, the sensor data may include oximeter data from theoximeter. The oximeter data may correspond to the heart rate of thepatient and the blood oxygen saturation of the patient for the timeassociated with the sensor data set. Body parameter values may include,but are not limited to, skin temperature, skin conductance, heart rate,change in heart rate, blood oxygen saturation, acceleration, respirationrate, and values determined as combinations thereof.

A depression-indicative value (DV) for a particular body parameter valueas a function of time may be calculated according to the equation:

DV_(PV)(t)=PV_(foreground)(t)/PV_(background)(t)  (Eqn 1)

where PV_(foreground)(t) is a moving average of a body parameter valueof interest, and PV_(background)(t) is a background value of the bodyparameter value of interest.

The value for PV_(foreground)(t) for many body parameter values may becalculated as a simple moving average using the equation:

$\begin{matrix}{{{PV}_{foreground}(t)} = {\frac{1}{WS} \cdot {\sum\limits_{k = s}^{{WS} + s}\; {{PV}\left( {t - k} \right)}}}} & \left( {{Eqn}\mspace{14mu} 2} \right)\end{matrix}$

where WS is a window size of the moving average, and

s is a step size.

Indications of depression episodes may occur gradually over relativelylong time periods (e.g., hours, days, or longer. Because of therelatively long periods of time needed to indicate depression episodes,the step size may range from seconds to a few minutes. The window sizemay be one or more hours, one or more days, or longer.

For some body parameter values (e.g., for values that are vectors), amoving average of a summed root-mean-squares may be used instead of thesimple moving average provided by Eqn 2. Values for PV_(foreground)(f)that are moving averages of summed root-mean-squares may be calculatedusing the equation:

$\begin{matrix}{{{PV}_{foreground}(t)} = {\frac{1}{WS}{\sum\limits_{k = s}^{{WS} + s}\; \sqrt{\left( {{PV}_{X}\left( {t - k} \right)} \right)^{2} + \left( {{PV}_{Y}\left( {t - k} \right)} \right)^{2} + \left( {{PV}_{Z}\left( {t - k} \right)} \right)^{2}}}}} & \left( {{Eqn}\mspace{14mu} 3} \right)\end{matrix}$

where PV_(X)(t−k) is an X-axis component of the body parameter value,PV_(Y)(t−k) is a Y-axis component of the body parameter value, andPV_(Z)(t−k) is a Z-axis component of the body parameter value.

For example, acceleration data received from a 3-axis accelerometer maybe passed through a bandpass filter (e.g., a 0.1 Hz to 20.0 Hz bandpassfilter) to obtain dynamic components for each axis (i.e., Acc_(X),Acc_(Y), and Acc_(Z)). The Acc_(foreground)(t) may be calculated as:

$\begin{matrix}{{{Acc}_{foreground}(t)} = {\frac{1}{WS}{\sum\limits_{k = s}^{{WS} + s}\; \sqrt{\left( {{Acc}_{X}\left( {t - k} \right)} \right)^{2} + \left( {{Acc}_{Y}\left( {t - k} \right)} \right)^{2} + \left( {{Acc}_{Z}\left( {t - k} \right)} \right)^{2}}}}} & \left( {{Eqn}\mspace{14mu} 4} \right)\end{matrix}$

The value for PV_(background)(t) may be calculated using the equation:

PV_(background)(t)=(1−FR)·PV_(background)(t−s)+FR·PV_(foreground)(t)  (Eqn5)

where FR is a forgetting factor.

Values of FR for particular body parameter values may be related to howquickly the body parameter values are able to return to an initial stateafter step changes. FR values may be values applicable to the generalpublic, values applicable to a subset of the general public (e.g., frompeople diagnosed as having a particular depression disorder), or valuesdetermined from sensor data for the patient 102.

In other embodiments, the depression-indicative value or the backgroundvalue of the body parameter value of interest may be calculated usingother equations. For example, in some embodiments, the value forPV_(background)(t) may be a baseline value for the body parameter valuefor the window size. Baseline values may initially be values applicableto the general public, values applicable to a subset of the generalpublic (e.g., from people diagnosed as having a particular depressiondisorder), or values determined from sensor data for the patient 102.The baseline values may indicate a healthy condition (i.e., when thepatient 102 does not experience one or more depression episodes during atime period of the window size). Baseline values may be updated based onhistorical data collected via the medical device system 100. Differentbaseline values may be used for different times of day, for differentstates of the patient 102 (e.g., awake or asleep), or both. In someembodiments, depression-indicative values may be calculated for variouswindow sizes (e.g., for an hour window, a day window, a week window, anda month window). For example, depression-indicative values for variouswindow sizes may be calculated using the equation:

$\begin{matrix}{{{DV}_{{Time},{PV}}(t)} = {\frac{1}{({Time}) \cdot {PV}_{{Time},{baseline}}} \cdot {\sum\limits_{k = s}^{{Time} + s}\; {{PV}\left( {t - k} \right)}}}} & \left( {{Eqn}\mspace{14mu} 6} \right)\end{matrix}$

where Time is a selected window size.

A depression detection value (DDV) may be determined from two or moredepression-indicative values for particular body parameter values. Forexample, the DDV may be a weighted sum of differentdepression-indicative values. Depression-indicative values correspondingto some body parameter values may be directly proportional to the DDV(e.g., skin temperature, skin conductivity, and heart rate) whiledepression-indicative values corresponding to other body parametervalues (e.g., blood oxygen saturation, step readings from a pedometer,and acceleration) may be inversely proportional to the DDV. Thedepression detection value as a function of time may be calculated usingthe equations:

$\begin{matrix}{{{DDV}(t)} = {{\sum\limits_{i}\; {w_{i}{{DV}_{i}(t)}}} + {\sum\limits_{j}\; {w_{j}\frac{1}{{DV}_{j}(t)}}}}} & \left( {{Eqn}\mspace{14mu} 7} \right)\end{matrix}$

and

Σ_(i) w _(i)+Σ_(j) w _(j)=1  (Eqn 8)

where w_(i) and w_(j) are weights,

-   -   values of i correspond to different body parameter values that        have depression-indicative values that are directly proportional        to the depression detection value, values of j correspond to        different body parameter values that have depression-indicative        values that are inversely proportional to the depression        detection value, and    -   values of DV_(i)(t) and DV_(j)(t) may be values calculated using        Eqn 1, Eqn 6, or both.        In some embodiments, initial values of the weights may be based        on sensor data obtained from the medical device system 100 for        the patient 102, based on data from a patient population that        have a condition similar to the patient 102, or based on other        factors. In some embodiments, the initial values of the weights        may be evenly distributed (e.g., if there are four weights, each        weight may initially have a value of 0.25) or may be unevenly        distributed. During use, the computation processor may adjust        values of the weights to improve detection of depression onset        and/or depression offset of depression episodes. For example,        the computation processor may determine subsequent rules for the        weights based on sensor data for the patient 102 and other data        that indicates depression onsets and depression offsets. The        other data may be based on input from the patient, based on        analysis of historic data, based on data from other sensors, or        combinations thereof.

As an example of the use of Eqn 7 and Eqn 8, the body parameter valuesmay correspond to skin temperature (ST), skin conductivity (SC), heartrate (HR), blood oxygen saturation (SpO2), and acceleration (Acc). Forthese body parameter values, Eqn 7 and Eqn 8 become:

$\begin{matrix}{{{DDV}(t)} = {{w_{ST} \cdot {{DV}_{ST}(t)}} + {w_{SC} \cdot {{DV}_{SC}(t)}} + {w_{HR} \cdot {{DV}_{HR}(t)}} + \frac{w_{{SpO}\; 2}}{{DV}_{{SpO}\; 2}(t)} + \frac{w_{Acc}}{{DV}_{Acc}(t)}}} & \left( {{Eqn}\mspace{14mu} 9} \right)\end{matrix}$

and

w _(ST) +w _(SC) +w _(HR) −w _(SpO2) =w _(Acc)=1  (Eqn 10)

Eqn 9 and Eqn 10 may be used to calculate depression detection valuesfor a particular sensor data set.

Physical activity level, an increased resting heart rate, decreasedheart rate variability, an increase in resting skin temperature, anincrease in resting skin conductance, an increase in resting respirationrate, and a decrease in blood oxygen saturation level may correlate withonset of a depression episode, severity of a depression episode, orboth. Data corresponding to physical activity may be provided by apedometer, by a 3-axis accelerometer, by a heart rate monitor, byanother type of sensor, or by combinations thereof. Sensor of themedical device 104, one or more sensors 118 coupled to the processingunit 106, or both, may provide sensor data used to determine physicalactivity level, resting heart rate, heart rate variability, skintemperature, skin conductance, respiration rate, blood oxygen saturationlevels, and other body parameter values that may be used to determine adepression state of the patient 102. One or more depression-indicativevalues corresponding to physical activity level, resting heart rate,heart rate variability, resting skin temperature, resting skinconductance, resting respiration rate, and blood oxygen saturation levelfor one or more window sizes may be used as depression-indicative valuesin Eqn 7 to determine the depression detection value.

One or more weight factors may be zero and corresponding data may beomitted from calculations of depression-indicative values when thepatient 102 is not in the resting state. Whether the patient 102 is in aresting state or an active state may be determined from sensor data fromone or more sensors. For example, an indication that the patient 102 isnot in the resting state may be that the heart rate of the patient 102is sustained above a heart rate of 100 beats per minute for longer thana minute. Other indications that the patient 102 is in an active statemay be provided by received pedometer data that indicates that thepatient 102 is moving and accelerometer data that indicates that thepatient 102 is active.

The computation processor may determine the depression detection valueperiodically (e.g., at intervals associated with a particular step size)based on sensor data received from the sensors coupled to the processingunit 106. The computation processor may determine a depression state foreach step based at least in part on a comparison of the depressiondetection value for the step to one or more thresholds. The depressionstate may be indicated by a depression onset value, a depression offsetvalue, or both. In an embodiment, the computation processor maycalculate a depression onset value and a depression offset value basedon a previous value of the depression onset, a previous value of thedepression offset, and based on a comparison of the current depressiondetection value to one or more thresholds. The depression onset valueand the depression offset value as a function of time may be determinedaccording to the equations:

$\begin{matrix}\left\{ \begin{matrix}{{{{on}(t)} = 0},} & {{{{off}(t)} = 0};} & {{{{if}\mspace{14mu} {{on}\left( {t - s} \right)}} = 0},} \\\; & \; & {{{off}\left( {t - s} \right)} = {{0\mspace{14mu} {and}\mspace{14mu} {{DDV}(t)}} < t_{on}}} \\{{{{on}(t)} = t},} & {{{{off}(t)} = 0};} & {{{{if}\mspace{14mu} {{on}\left( {t - s} \right)}} = 0},} \\\; & \; & {{{off}\left( {t - s} \right)} = {{0\mspace{14mu} {and}\mspace{14mu} {{DDV}(t)}} \geq t_{on}}} \\{{{{on}(t)} = {NC}},} & {{{{off}(t)} = 0};} & {{{{if}\mspace{14mu} {{on}\left( {t - s} \right)}} \neq 0},} \\\; & \; & {{{off}\left( {t - s} \right)} = {{0\mspace{14mu} {and}\mspace{14mu} {{DDV}(t)}} \geq t_{off}}} \\{{{{on}(t)} = {NC}},} & {{{{off}(t)} = t};} & {{{{if}\mspace{14mu} {{on}\left( {t - s} \right)}} \neq 0},} \\\; & \; & {{{off}\left( {t - s} \right)} = {{0\mspace{14mu} {and}\mspace{14mu} {{DDV}(t)}} < t_{off}}} \\{{{{on}(t)} = 0},} & {{{{off}(t)} = 0};} & {{{{if}\mspace{14mu} {{on}\left( {t - s} \right)}} \neq 0},} \\\; & \; & {{{off}\left( {t - s} \right)} \neq {0\mspace{14mu} {and}\mspace{14mu} {{DDV}(t)}} < t_{off}}\end{matrix} \right. & \left( {{Eqn}\mspace{14mu} 11} \right)\end{matrix}$

where t is the elapsed time,

-   -   s is the step size,    -   on(t) is the depression onset at time t,    -   off(t) is the depression offset at time t,    -   “NC” stands for “not changed,”    -   T_(on) is the threshold value for depression onset, and    -   T_(off) is the threshold value for depression offset.

Table 1 depicts example data generated during application of Eqn 11. Asindicated in the first row of Table 1, the depression onset value (i.e.,on(t)) and the depression offset value (i.e., off(t)) may initially bezero, which may indicate that no depression episode is occurring. Sensordata may be sent to the computation processor for an initial number ofsteps of the step size to enable the computation processor to determinean initial DDV(t) value. The time corresponding to the initial DDV(t)value is set as s. As indicated in the first row and the second row ofTable 1, the initial DDV(t) value is less than the threshold fordepression onset (i.e., T_(on)), and the values of the depression onsetand the depression offset at the previous time (i.e., at time 0) arezero when the time is s. Since the values of the previous depressiononset and the previous depression offset are zero, a comparison ofDDV(t) to the threshold value for depression offset (i.e., T_(off)) isnot needed. According to the first line of Eqn 11, the values of thedepression onset and the depression offset at time s remain zero.Similarly, as indicated in the second row and the third row of Table 1,the DDV(t) value is less than the threshold for depression onset, andthe values of the depression onset and the depression offset at theprevious time (i.e., at time s) are zero when the time is 2s. Accordingto the first line of Eqn 11, the values of the depression onset and thedepression offset at time 2s remain zero.

TABLE 1 DDV(t) and T_(on) DDV(t) and T_(off) Row # t comparisoncomparison on(t) off(t) 1 0 0 0 2   s DDV(t) < T_(on) not needed 0 0 3 2s DDV(t) < T_(on) not needed 0 0 4 3 s DDV(t) > T_(on) not needed 3 s 05 4 s not needed DDV(t) > T_(off) 3 s 0 6 5 s not needed DDV(t) >T_(off) 3 s 0 7 6 s not needed DDV(t) < T_(off) 3 s 6 s 8 7 s not neededDDV(t) < T_(off) 0 0 9 8 s DDV(t) < T_(on) not needed 0 0

As indicated in the third row and the fourth row of Table 1, the DDV(t)value is greater than the threshold for depression onset, and the valuesof the depression onset and the depression offset at the previous time(i.e., at time 2s) are zero when the time is 3s. According to the secondline of Eqn 11, the value of the depression onset at time 3s becomes 3sand the value of the depression offset at time 3s remains zero. Untilthe depression onset value is returned to zero, a comparison of DDV(t)to the threshold value for depression onset is not needed, but acomparison of DDV(t) to the threshold value for depression offset isneeded. Because the depression onset value changed from zero to anon-zero value, the processing unit 106 may initiate closed-looptherapy, depending on an operational mode setting of the processing unit106.

As indicated in the fourth row and the fifth row of Table 1, the DDV(t)value is greater than the threshold for depression offset, the value ofthe depression onset at the previous time (i.e., at time 3s) is notzero, and the value of the depression offset at the previous time iszero when the time is 4s. According to the third line of Eqn 11, thevalue of the depression onset at time 4s remains 3s and the value of thedepression offset at time 4s remains zero. Similarly, as indicated inthe fifth row and the sixth row of Table 1, the DDV(t) value is greaterthan the threshold for depression offset, the value of the depressiononset at the previous time (i.e., at time 4s) is not zero, and the valueof the depression offset at the previous time is zero when the time is5s. According to the third line of Eqn 11, the value of the depressiononset at time 5s remains 3s and the value of the depression offset attime 5s remains zero.

As indicated in the sixth row and the seventh row of Table 1, the DDV(t)value is less than the threshold for depression offset, the value of thedepression onset at the previous time (i.e., at time 5s) is not zero,and the value of the depression offset at the previous time is zero whenthe time is 6s. According to the fourth line of Eqn 11, the value of thedepression onset at time 6s remains 3s and the value of the depressionoffset at time 6s becomes 6s. Because the depression offset valuechanged from zero to a non-zero value, the processing unit 106 may ceaseclosed-loop therapy, depending on a mode setting of the processing unit106.

As indicated in the seventh row and the eighth row of Table 1, theDDV(t) value is less than the threshold for depression offset, the valueof the depression onset at the previous time (i.e., at time 6s) is notzero, and the value of the depression offset at the previous time is notzero when the time is 7s. According to the fifth line of Eqn 11, thevalues of the depression onset and the depression offset at time 7s arereset to zero. As indicated in the eighth row and the ninth row of Table1, the DDV(t) value is less than the threshold for depression onset andthe values of the depression onset and the depression offset at theprevious time (i.e., at time 7s) are zero when the time is 8s. Accordingto the first line of Eqn 11, the values of the depression onset and thedepression offset at time 8s remain zero.

In some embodiments, the depression detection value may be compared toone or more pre-depression episode thresholds to determine whether thedepression detection value for the patient 102 is approaching a valuethat indicates depression onset. When the comparison of the depressiondetection value to the one or more pre-depression episode thresholds issatisfied, a notification may be sent to the processing unit 106, thehealthcare provider, or both. The notification sent to the processingunit 106 may include a visual notification, an audio notification, ahaptic notification, or combinations thereof. The notification sent tothe processing unit 106 may inform the patient 102 that the patient 102may be approaching a depressive state. The notification may provide thepatient 102 with one or more suggested courses of action to inhibit thepatient 102 from reaching the depressive state. The suggestions mayinclude, but are not limited to, initiate exercise, interact withpeople, walk, initiate a conversation with a friend or acquaintance, orcombinations thereof. The notification sent to the healthcare providermay include an electronic mail, a text, a telephone call, or anotherform of communication. The notification may inform the healthcareprovider that the patient 102 may be approaching a depressive state. Arepresentative of the healthcare provider may contact the patient 102 inresponse to the notification to inquire about the condition of thepatient 102 and facilitate a course of action by the patient 102 thatinhibits onset of or reduces severity of the depressive state.

In some embodiments, one or more values (e.g., body parameter values,moving averages of one or more body parameter values, ordepression-indicative values corresponding to particular body parametervalues) may provide reasonably reliable indications of depression onset,depression offset, or both. However, such values may provide some falseindications of depression onset or depression offset. The one or morevalues may be used in conjunction with the depression detection valuedetermined from Eqn 7 to determine depression onset values anddepression offset values. The determined depression onset values anddepression offset values may have fewer false indications than eitherdepression onset values and depression offset values determined onlyfrom the one or more values or from depression onset values anddepression offset values determined only from the depression detectionvalues.

When a change in the depression state of the patient 102 indicates onsetof a depression episode, the medical device system 100 may initiateclosed-loop therapy to stop the depression episode, limit an intensityof the depression episode, limit a duration of the depression episode,or combinations thereof. In some embodiments, the closed-loop therapymay include CNS signals sent from the processing unit 106 to the medicaldevice 104 to provide CNS to the patient 102. The closed-loop therapyCNS signals may be stronger than scheduled CNS signals applied to thepatient 102 to inhibit the occurrence of a depression episode. Theclosed-loop therapy CNS signals may be based on determined depressionepisode detection values. For example, in a simple embodiment, only thestimulation current of scheduled CNS signals is adjusted to form theclosed-loop therapy CNS signals.

$\begin{matrix}{I_{resp} = \left\{ \begin{matrix}{{I_{norm} \cdot {{DDV}(t)}};} & {{{{if}\mspace{14mu} {I_{norm} \cdot {{DDV}(t)}}} < {{maxI}_{resp}\mspace{14mu} {and}}}\mspace{14mu}} \\\; & {{I_{norm} \cdot {{DDV}(t)}} > {minI}_{resp}} \\{{maxI}_{resp};} & {{{if}\mspace{14mu} {I_{norm} \cdot {{DDV}(t)}}} \geq {maxI}_{resp}} \\{{minI}_{resp};} & {{{if}\mspace{14mu} {I_{norm} \cdot {{DDV}(t)}}} \leq {maxI}_{resp}}\end{matrix} \right.} & \left( {{Eqn}\mspace{14mu} 12} \right)\end{matrix}$

Eqn 12 enables application of a response stimulation current that variesfrom a minimum stimulation current (e.g., a stimulation current equal toor above the scheduled stimulation current) to a maximum stimulationcurrent depending on a determined value of the depression detectionvalue. In other embodiments, other parameters of the scheduled CNSsignals (e.g., signal polarity, pulse width, and pulse period) may beadjusted in lieu of or in conjunction with adjustment of the stimulationcurrent. In other embodiments, the closed-loop stimulation may comprisemicro-burst stimulation, electrical stimulation in combination withother forms of therapy (e.g., drug/medication delivery), stimulation ofmultiple cranial nerves (e.g., the vagus, trigeminal, hypoglossal,glossopharyngeal), stimulation of left and right cranial nerves,simultaneous or coordinated (e.g., interleaved) stimulation at multiplenerve sites, one or more signal parameters randomized within a range, ora combination thereof. Micro-burst stimulation may include a signalhaving 2-10 pulses per burst where the pulses are provided at afrequency in the range of 100-300 Hertz.

In some embodiments, additional thresholds may be provided to indicatethe severity of the depression episode. For example, one or morethreshold values beyond the onset threshold may be provided and may becompared to the depression detection value, DDV(t), to determine theseverity of the depression episode being experienced. If the depressiondetection value triggers one or more of the threshold values, certainactions may be initiated. For example, the therapy may be modified byincreasing the current amplitude per pulse, adjusting variousstimulation parameters (e.g., frequency, duty-cycle, off-time, on-time,pulse width, number of pulses, inter-pulse interval), randomizingcertain signal parameters within a range, providing other forms oftherapy (e.g., drug/medication delivery), stimulating multiple cranialnerves (e.g., the vagus, trigeminal, hypoglossal, glossopharyngeal),stimulating left and right cranial nerves, providing simultaneous orcoordinated (e.g., interleaved) stimulation at multiple nerve sites, ora combination thereof. In addition, once a threshold is triggered, thevalue needed to return to the previous state (the state beforetriggering the threshold) may be offset from the threshold value(hysteresis) to avoid switching between states too frequently. Warningsor notifications may also be provided to a physician, patient,caregiver, monitoring service, or a combination thereof, when thedepression detection value triggers one or more of the thresholds.

In some embodiments, the processing unit 106 may configured to store andtrend one or more of the body parameter values as a function of time andone or more trend thresholds. When the trend satisfies one or more ofthe trend thresholds, the processing unit 106 may be provide arecommendation to the patient or a caregiver to take one or more actionsto inhibit the depression episode. Trending may also be applied to theone or more depression-indicative values and the depression detectionvalues to monitor the patient's progress overtime.

FIG. 2 is a schematic representation of a particular embodiment of anexternal medical device 200 to treat a patient that has been diagnosedas having a depression disorder (e.g., major depressive disorder,dysthymia, seasonal affective disorder, and postpartum depression). Themedical device 200 may be the medical device 104 depicted in FIG. 1. Themedical device 200 may include a base 202. The base 202 may be anadhesive patch that is placed against the skin of the patient.Alternately, the base 202 may be, or may be coupled to, an article ofclothing that the patient wears. The article of clothing may be aheadband, hat, scarf, other item, or a combination thereof.

The medical device 200 may include electrode pairs 204. Each electrodepair 204 may include a positive electrode 206 and a negative electrode208. The electrode pairs 204 may enable application of CNS to thepatient via a processing unit coupled to the medical device 200. The CNSmay include transcutaneous TNS. Alternately, the CNS may includesubcutaneous TNS via activation of one or more subcutaneously positionedelectrode pairs by one or more of the electrode pairs 204.Alternatively, or in addition to enabling CNS, the electrode pairs 204may enable the medical device 200 to collect skin conductance data. Anelectrically conductive contact gel or other electrically conductivematerial may be placed on contacts of the electrode pairs 204 beforeattachment of the medical device 200 to the patient to ensure goodelectrical contact between the electrode pairs 204 and the patient.

The medical device 200 may include a temperature sensor 210. Thetemperature sensor 210 may enable the medical device 200 to collect skintemperature data. In an embodiment, the temperature sensor 210 may be incontact with skin of the patient when the medical device 200 is attachedto the patient. A thermally conductive gel or other thermally conductivematerial may be placed on the temperature sensor 210 before attachmentof the medical device 200 to the patient to ensure good thermal contactbetween the temperature sensor 210 and the patient. In otherembodiments, the temperature sensor 210 may be an optical temperaturesensor or other type of temperature sensor that does not need to be inthermal contact with the patient.

The medical device 200 may include an oximeter 212. The oximeter 212 mayenable the medical device 200 to collect oximeter data. The oximeterdata may enable determination of patient blood oxygen saturation,patient heart rate, or both. The oximeter 212 may be a reflectanceoximeter that detects reflections of light from a first light source ata first wavelength (e.g., a 905 nanometer (nm) light emitting diode(LED)) and a second light source at a second wavelength (e.g., a 660 nmLED). The oximeter 212 may be in contact with, or in proximity to, skinof the patient when the medical device 200 is attached to the patient.

The medical device 200 may also include a three axis accelerometer 214.The three axis accelerometer 214 may enable the medical device tocollect acceleration data to the processing unit.

The medical device 200 may be communicatively coupled to a processingunit, such as the processing unit 106 depicted in FIG. 1. The medicaldevice 200 may include a wired connection 216, one or more wirelessconnections, or both, to the processing unit to enable data from theelectrode pairs 204, the temperature sensor 210, the oximeter 212, thethree axis accelerometer 214, or a combination thereof, to be sent tothe processing unit and to enable CNS signals received from theprocessing unit to be applied to the patient via the electrode pairs204. The wired connection 216, one or more transceivers that enablewireless communication, or both, may be coupled to a bus that iselectrically connected to the electrode pairs 204, the temperaturesensor 210, the oximeter 212, the three axis accelerometer 214, or acombination thereof.

FIG. 2 depicts the medical device 200 with the electrode pairs 204 andthree other sensors. In other embodiments, the medical device 200 mayinclude fewer sensors, more sensors, or different sensors. For example,the medical device 200 may include a pedometer. In an embodiment, themedical device 200 does not include the oximeter 212. In thisembodiment, the oximeter data may be provided by a transmittanceoximeter or reflectance oximeter attached to the patient andcommunicatively coupled to the processing unit.

FIG. 3 is a flow chart of a first particular embodiment of a method ofusing cranial nerve stimulation (CNS) to treat a patient diagnosed witha depression disorder (e.g., major depressive disorder, dysthymia,seasonal affective disorder, and postpartum depression). The CNS mayinclude trigeminal nerve stimulation, vagus nerve stimulation,stimulation of other cranial nerves, or combinations thereof. The CNSmay be applied by an external medical device coupled to a patient (e.g.,the medical device system 100 depicted in FIG. 1), an implanted medicaldevice, or a combination thereof. In an embodiment, the method may beperformed by a processor. The processor may be the processor of theexternal medical device or the processor of a device that receives thesensor data from the external medical device (e.g., the processor of acomputer system associated with a healthcare provider). At 302, sensordata is received at the processor from sensors of the external medicaldevice that enables application of CNS signals to the patient. Thesensor data corresponds to at least a first body parameter value for thepatient and a second body parameter value for the patient. The sensordata may be received as sensor data sets. Each sensor data set maycorrespond to a particular time and may include data associated withsensors of the external medical device. The sensors may include atemperature sensor, a conductivity sensor, an oximeter, a three axisaccelerometer, a respiration sensor, a blood pressure sensor, apedometer, other sensors, or combinations thereof.

The sensor data may include body parameter values or may enable theprocessor to calculate body parameter values corresponding to particularsensor data. For each sensor data set, the processor may store a timeassociated with the sensor data, the sensor data, values calculatedbased on the sensor data, or combinations thereof, as historic data. Thevalues may include body parameter values, moving averages of bodyparameter values, change rates of body parameter values based on one ormore previous sensor data sets, depression-indicative values, depressiondetection values, values associated with a depression state, or anycombination thereof.

A first depression-indicative value based on the first body parametervalue and a second depression-indicative value based on the second bodyparameter value may be determined by the processor, at 304. For example,the first depression-indicative value and the seconddepression-indicative value may be determined via application of Eqn 1and Eqn 6.

A depression detection value may be determined by the processor, at 306.For example, the depression detection value may be determined as afunction of a first weight applied to the first depression-indicativevalue and a second weight applied to the second depression-indicativevalue. The processor may determine the depression detection value byapplication of Eqn 7 and Eqn 8.

A depression state may be determined by the processor based at least inpart on a comparison of the depression detection value to one or morethreshold values, at 308. The one or more threshold values may include adepression onset threshold, a depression offset threshold, or both. Insome embodiments, the depression onset threshold is the same as thedepression offset threshold. The depression state may include adepression onset value and a depression offset value. The depressiononset value and the depression offset value of the depression state maybe calculated using Eqn 11.

In an embodiment, the depression onset value and the depression offsetvalue may initially be zero, indicating that no depression episode isoccurring. When a comparison of the depression detection value to thedepression onset threshold is satisfied (e.g., the depression detectionvalue is greater than or equal to the depression onset threshold), thedepression onset value may be set to the time associated with the dataset. When the depression onset value is not zero, the depression onsetvalue may indicate the onset time of a depression episode. The value ofthe depression onset value may remain at that value until a comparisonof depression detection values of two consecutive subsequent data setsto the depression offset threshold is satisfied (e.g., the depressiondetection values calculated from two consecutive sensor data sets areless than the depression offset threshold). The value of the depressionoffset value may be changed to a time associated with the first sensordata set of the two consecutive data sets to indicate a time when thedepression episode is considered to be finished. The values of thedepression onset value and the depression offset value may be both resetto zero in response to the depression detection value calculated fromthe second sensor data set satisfying the depression offset threshold.

In some embodiments, the external medical device may be configured toreceive patient input. The medical device may provide an indication ofwhen a change in the determined depression state occurs. The indicationmay be a visual indication, an audio indication, a vibrationalindication, or combinations thereof. The patient may provide user inputthat indicates whether the patient agrees with the indication. Forexample, the processor may determine that a change in the depressiononset value indicates onset of a depression episode at 3:05 p.m. on aparticular day and a corresponding depression offset value at 5:30 p.m.on the particular day. At 5:35 p.m. on the particular day or at someother time, the processor may provide the notification to the patientand query the patient regarding whether the patient agrees that adepression episode occurred in the time frame between 3:05 and 5:30.User input in response to the query may be stored as patient input data.In some embodiments, the patient may be asked to keep track of onsetsand offsets of depression episodes. The data kept by the patient may beentered into a computer and transmitted to the processor as patientinput data.

The processor, another computing system, or both may be used to analyzehistoric data and patient input data for a particular time period. Thetime period may be a number of a week, a month, or other time period. Apresentation of values obtained from the historic data as a function oftime along with patient input data that indicates depression episodeoccurrences may be reviewed by one or more healthcare professionals, thepatient, or both. The presentation may include graphs, charts, tabulateddata, other formats of data presentation, or combinations thereof.Depression input may be received from one or more people reviewing thepresentation. The depression input may indicate when depression onsetsand depression offsets occurred based on the historic data.

The depression input may be sent to or received by the processor. Thedepression input may be compared by the processor to depression statesdetermined by the processor to determine indications from the depressioninput that agree with or contradict the depression onset values and thedepression offset values of the depression states determined by theprocessor. In response to an indication that contradicts a depressionstate determined by the processor, the processor may automaticallyadjust at least one of the first weight and the second weight, at 310.Adjustment of at least one of the first weight and the second weight mayenable determined values of the depression state to more closely conformto the depression states indicated by the depression input. For example,depression input compared with determined depression states when thebody parameter values are in certain ranges may indicate about 50%agreement regarding depression onset when the values of the first weightand the second weight are each at 0.25. At least one other weight factorallows the sum of all of the weight factors to add up to one, as in Eqn8. Calculations performed by the processor may adjust the first weightto 0.7 and the second weight to 0.15 when the particular body parametervalues are in the certain ranges to improve agreement between thedetermined depression states and the corresponding depression input ofabout 80%. The processor may perform statistical analysis, may determinedepression states using various values for the first weight and thesecond weight, may employ other techniques, or may use combinationsthereof, to determine the values of the first weight, the second weight,and other weights used to determine the depression states. After theweights are adjusted, the new weights may be used to determinesubsequent depression states (e.g., depression onset values, depressionoffset values, or both).

Scheduled CNS signals may be applied to the patient via the externalmedical device, at 312. Scheduled CNS may inhibit the occurrence ofdepression episodes in the patient. Closed-loop therapy may be appliedto the patient via the external medical device upon determination of adepression state that indicates depression onset, at 314. Theclosed-loop therapy may limit depression episode duration, depressionepisode severity, or both. The closed-loop therapy may include CNSsignals, activation of a medicine delivery system, other treatmentoption, or combinations thereof.

In an embodiment, the closed-loop therapy may include one or more CNSsignals that replace one or more scheduled CNS signals. In anotherembodiment, the closed-loop therapy includes adjusting at least oneparameter of the one or more CNS signals to be different than acorresponding parameter of the scheduled CNS signals. The parameters mayinclude, but are not limited to, amplitude, polarity, frequency, pulsewidth, pulse period, duty cycle, charge balancing, and signal duration.The CNS signals used for closed-loop therapy may be stronger than thescheduled CNS signals. For example, the at least one parameter may bestimulation current amplitude. The stimulation current amplitude may beadjusted between a minimum stimulation current amplitude and a maximumcurrent stimulation amplitude based on a depression detection valuecalculated from a most recently received sensor data set. Theclosed-loop therapy may abort a depression episode or reduce depressionepisode intensity, depression episode duration, or both. Closed-looptherapy may be terminated when the depression state indicates offset ofthe depression episode, at 316. The method may end, at 318.

FIG. 4 is a flow chart of a second particular embodiment of a method ofusing CNS to treat a patient diagnosed with a depression disorder (e.g.,major depressive disorder, dysthymia, seasonal affective disorder, andpostpartum depression). The CNS may be applied by an external medicaldevice coupled to a patient (e.g., the medical device system 100depicted in FIG. 1). In an embodiment, the method may be performed by aprocessor. The processor may be the processor of the external medicaldevice or the processor of a device that receives sensor data from theexternal medical device (e.g., the processor of a computer systemassociated with a healthcare provider). At 402, sensor data from sensorsof an external medical device is received. The sensor data correspondsto at least a first body parameter value for the patient and a secondbody parameter value for the patient. The sensor data may be received assensor data sets. Each sensor data set may correspond to a particulartime and may include data associated with sensors of the externalmedical device. The sensors may include a temperature sensor, aconductivity sensor, an oximeter, a three axis accelerometer, arespiration sensor, a blood pressure sensor, a pedometer, other sensors,or combinations thereof.

The sensor data may include body parameter values or may enable theprocessor to calculate body parameter values corresponding to particularsensor data. For each sensor data set, the processor may store a timeassociated with the sensor data, the sensor data, values calculatedbased on the sensor data, or combinations thereof, as historic data. Thevalues may include body parameter values, moving averages of bodyparameter values, change rates of body parameter values based on one ormore previous sensor data sets, depression-indicative values, depressiondetection values, values associated with a depression state, or anycombination thereof.

A first depression-indicative value based on the first body parametervalue and a second depression-indicative value based on the second bodyparameter value may be determined, at 404. For example, the firstdepression-indicative value and the second depression-indicative valuemay be determined via application of Eqn 1 and Eqn 6.

A depression detection value may be determined by the processor, at 406.For example, the depression detection value may be determined as afunction of a first weight applied to the first depression-indicativevalue and a second weight applied to the second depression-indicativevalue. The processor may determine the depression detection value byapplication of Eqn 7 and Eqn 8. At least a value for the first weightmay depend on one or more values determined from the sensor data. Forexample, when the patient is exercising, sensor data may indicate thatthe heart rate of the patient is at a rate of over one hundred beats perminute for a relatively long period of time (e.g., for over 5 minutes ormore), sensor data for skin temperature may be higher than a normalvalue, and sensor data for skin conductance may be a relatively largevalue. When the heart rate value is sustained over 100 beats per minutefor the relatively long period of time, values of weights correspondingto the heart rate, the skin temperature, the respiration rate, and theskin conductance may be reduced and values of other weight factors(e.g., the weight corresponding to the blood oxygen saturation, theweight corresponding to the pedometer reading, the weight correspondingto the acceleration, or combinations thereof) may be increased. Thevalues determined for the weights may be based on sets of rules thatdefine a patient state. Rules may be set to accommodate various normalactivities and lack of activity for the patient (e.g., rules toaccommodate exercising, walking, and resting) and for the time of day.For example, a patient state may be defined by a set of rules for asleep state, which may include rules associated with time of day, heartrate data, and accelerometer data. A patient state may also be definedfor being awake, resting, exercising, or various other states. Thevalues of the weights may be adjusted when the rules are met to enterand exit these patient states. Since different parameters may be more orless indicative of depression in each of these patient states, theweights associated with the different body parameters may be adjusted tomore accurately detect a depression state. Time of day may also be usedto adapt the weights to the patient's natural circadian rhythms,especially if the patient is more likely to experience depression atcertain times of the day (e.g., night, morning, mid-day, evening). Theweights may also be adjusted based on the time of year and/or theambient temperature. For example, depression may be more common in thewinter when the day light hours are fewer and the temperature is lower.The weights may also be adjusted based on a medication schedule and thetype of medications being taken. For example, certain body parametervalues may be less likely to indicate a depression state shortly after acertain medication is taken, but hours later the medication may haveless of an effect on the body parameter.

A depression state may be determined based at least in part on acomparison of the depression detection value to one or more thresholdvalues, at 408. The one or more threshold values may include adepression onset threshold, a depression offset threshold, or both. Insome embodiments, the depression onset threshold is the same as thedepression offset threshold. The depression state may be indicated by adepression onset value, a depression offset value, or both. Thedepression onset value and the depression offset value of the depressionstate may be determined using Eqn 11.

Closed-loop therapy may be initiated when the depression state indicatesonset of a depression episode, at 410. The closed-loop therapy mayinclude CNS signals, activation of a medicine delivery system, othertreatment option, or combinations thereof. Closed-loop therapy may beterminated when the depression state indicates offset of the depression,at 412.

Historic data may be sent to an interface, at 414. The historic data maycorrespond to patient depression state indications, other values, or acombination thereof. The other values may include the firstdepression-indicative values, the second depression-indicative values,the depression detection values, the depression states, or a combinationthereof. The patient depression state indications correspond to patientinput that indicates when the patient experienced onset of a depressionepisode, offset of a depression episode, or both. The patient depressionstate indications may include patient input that confirms or contradictsdetermined depression states. The patient input may be received inresponse to a query from the external medical device. The interface mayinclude a user interface that enables user input related to the historicdata.

The historic data may be analyzed by the patient, by personnelassociated with a healthcare provider, by one or more computers systems,or combinations thereof. Depression input data that indicates when oneor more depression episodes occurred during the particular time periodbased on the historic data may be received, at 416. One or more rules todetermine at least the value of the first weight based on the one ormore body parameter values may be established when data from thedepression input data contradicts one or more determined depressionstates to enable determined depression states to be in closer agreementwith the depression input data, at 418. Establishing the one or morerules may include generating new rules, adjusting existing rules, orboth. Statistical analysis, determining depression states using variousweight values, other techniques, or combinations thereof may be used toestablish the one or more rules.

For example, depression-indicative-values for four body parameter valuesmay be determined from sensor data. A first rule may establish that eachweight used in calculating the depression detection value based ondepression-indicative values is 0.25 when the heart rate of the patientis in a normal range for the patient (e.g., from 50 beats per minute(bpm) to 65 bpm). Historic data for a month may indicate that depressionstate determinations using values of 0.25 for each weight agree with thedepression input data about 60% of the time when the heart rate for thepatient was in the normal range. Manipulation of the weights toalternate values (e.g., 0.55 for a first weight, 0 for a second weight,0.40 for a third weight, and 0.05 for a fourth weight) may enabledetermined depression states using data that corresponds to when theheart rate of the patient was in the normal range during the particulartime period to completely agree with the depression input data forcorresponding times. In this example, the first rule may be adjusted sothat the weight values are the alternate values that result in agreementwith the depression input data.

In addition to enabling the change or addition of rules, the historicdata may be used to change other values used during determination ofdepression states. For example, examination of the historic data mayindicate to a healthcare provider that the one or more threshold valuesused to calculate the depression state should be adjusted. In thisexample, the healthcare provider may enter data that changes the one ormore threshold values.

Input to change scheduled CNS signals based on the historic data may bereceived, at 420. For example, the input may be received from healthcarepersonnel. The input may change one or more parameters that define thescheduled CNS signals. For example, a healthcare provider may determinethat depression onset occurs too frequently for the patient. In responseto the determination, the healthcare provider may provide input tochange one or more scheduled CNS signal parameters to shorten the timeperiod between the scheduled CNS signals (e.g., from once per hourduring waking hours to once per 45 minutes during waking hours). One ormore changes may be implemented to the scheduled CNS signals based onthe input, at 422. For example, the input may be sent to the externalmedical device and appropriate parameters of the scheduled CNS signalsmay be changed to new values to enable the external medical device toprovide the scheduled CNS signals with the shortened time period betweenscheduled CNS signals as requested by the healthcare provider. Themethod may end, at 424.

Various embodiments disclosed herein enable a medical device system thatis located substantially external to a patient to provide open-looptherapy and closed-loop therapy to the patient. One or more componentsof the medical device system (e.g., a vagus nerve stimulation system,one or more subcutaneous electrodes, at least a portion of a medicinedelivery system, or combinations thereof) may be implanted in thepatient. The medical device system enables application of open-looptherapy for the patient and closed-loop therapy for the patient.Open-loop therapy may be applied to the patient at intervals to inhibitdepression episode occurrence, to reduce intensity of depressionepisodes that occur, to reduce duration of depression episodes thatoccur, or combinations thereof. Closed-loop therapy may be applied tothe patient upon a determination of depression onset. Closed-looptherapy may be stopped upon a determination of depression offset. Theclosed-loop therapy may stop a depression episode, limit depressionepisode intensity, limit depression episode duration, or combinationsthereof. Depression onset may be determined based on collected sensordata from the medical device system.

The medical device system may use historical data collected by themedical device system and depression indication data to customize themedical device system for the patient by adjusting values used todetermine depression states (e.g., weight factors), by adjusting orcreating rules used to determine depression states, by adjustingthresholds used to determine depression states, or combinations thereof.The depression indication data may be provided by the patient, byhealthcare provider personnel, or both, based on analysis of thehistorical data. Customizing the medical device system for the patientmay inhibit applications of closed-loop therapy when the patient is notexperiencing depression onset.

The disclosure is described above with reference to drawings. Thesedrawings illustrate certain details of specific embodiments of thesystems and methods and programs of the present disclosure. However,describing the disclosure with drawings should not be construed asimposing on the disclosure any limitations that may be present in thedrawings. The present disclosure contemplates methods, systems, andinstructions on non-transitory computer-readable media that areexecutable by a processor for accomplishing particular tasks. Theprocessor may be a general computer processor, a special purposecomputer processor, or a hardwired system.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Accordingly, the disclosure and the figures are to be regarded asillustrative rather than restrictive. Although specific embodiments havebeen illustrated and described herein, it should be appreciated that anysubsequent arrangement designed to achieve the same or similar purposemay be substituted for the specific embodiments shown. This disclosureis intended to cover any and all subsequent adaptations or variations ofvarious embodiments.

The Abstract of the Disclosure is provided with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. In addition, in the foregoing Detailed Description, variousfeatures may be grouped together or described in a single embodiment forthe purpose of streamlining the disclosure. This disclosure is not to beinterpreted as reflecting an intention that the claimed embodimentsrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter may bedirected to less than all of the features of any of the disclosedembodiments. Thus, the following claims are incorporated into theDetailed Description, with each claim standing on its own as definingseparately claimed subject matter.

The above-disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe scope of the present disclosure. Thus, to the maximum extent allowedby law, the scope of the present disclosure is to be determined by thebroadest permissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

What is claimed is:
 1. A method comprising: receiving sensor data at aprocessor from one or more sensors of an external medical device,wherein the sensor data corresponds to at least a first body parametervalue for a patient and a second body parameter value for the patient;determining, via the processor, a first depression-indicative valuebased on the first body parameter value and a seconddepression-indicative value based on the second body parameter value;determining, via the processor, a first weight and a second weight;determining, via the processor, a depression detection value as afunction of, in part, the first weight applied to the firstdepression-indicative value and the second weight applied to the seconddepression-indicative value; and determining, via the processor, adepression state based at least in part on a comparison of thedepression detection value to a first threshold value and a secondthreshold value different from the first threshold value, the firstthreshold value associated with an onset of a depression episode, thesecond threshold value associated with an offset of the depressionepisode.
 2. The method of claim 1, further comprising automaticallyadjusting, via the processor, at least one of the first weight or thesecond weight in response to an indication contradicting the depressionstate.
 3. The method of claim 2, wherein the indication is included indepression data, the depression data comprising at least one of patientinput data, historic data corresponding to values as a function of time,or second sensor data from one or more sensors.
 4. The method of claim1, further comprising, in response to determining that the depressionstate indicates depression onset, initiating, via the processor,closed-loop therapy by one or more electrodes.
 5. The method of claim 4,wherein the closed-loop therapy comprises cranial nerve stimulation(CNS), and wherein the CNS comprises external trigeminal nervestimulation (TNS), vagus nerve stimulation (VNS), or a combinationthereof.
 6. The method of claim 1, further comprising adjusting, via theprocessor, the first weight based on a state of current physicalactivity of the patient relating to a current level of exercise of thepatient.
 7. The method of claim 6, wherein the state of activitycomprises one or more of an awake state, a resting state, a sleepingstate, or an exercise state.
 8. The method of claim 1, furthercomprising setting, via the processor, an initial value of the firstweight and an initial value of the second weight based on an input froma health care provider, based on the sensor data, based on historicdata, or based on a selected patient population.
 9. The method of claim1, wherein determining the first depression-indicative value comprisescalculating, via the processor, the first depression-indicative value asa ratio of a first moving average value of the first body parametervalue over a first time period to a second moving average value of thefirst body parameter value over a second time period.
 10. The method ofclaim 1, wherein the first body parameter value is a skin temperature,skin conductance, heart rate, change in heart rate, blood oxygensaturation, acceleration, respiration rate, or a value determined as acombination thereof.
 11. The method of claim 1, further comprising:comparing, via the processor, a trend of one or more body parametervalues as a function of time to at least one trend threshold; andoutputting, via the processor, a recommendation when the trend satisfiesat least one trend threshold, wherein the recommendation is associatedwith a prompt to take one or more actions to inhibit a depressionepisode in the patient.
 12. A system comprising: a processing unitcomprising a processor and a memory storing instructions that areexecutable by the processor to: receive first sensor data correspondingto a first body parameter value for a patient from a first sensor;receive second sensor data corresponding to a second body parametervalue for the patient from a second sensor; determine a firstdepression-indicative value based on the first body parameter value anddetermine a second depression-indicative value based on the second bodyparameter value; determine a first weight and a second weight; determinea depression detection value as a function of, in part, the first weightapplied to the first depression-indicative value and the second weightapplied to the second depression-indicative value; and determine adepression state based at least in part on a comparison of thedepression detection value to a first threshold value and a secondthreshold value different from the first threshold value, the firstthreshold value associated with an onset of a depression episode, thesecond threshold value associated with an offset of the depressionepisode.
 13. The system of claim 12, wherein the instructions arefurther executable by the processor to adjust at least one of the firstweight or the second weight in response to an indication contradictingthe depression state.
 14. The system of claim 12, wherein theinstructions are further executable by the processor to, in response todetermining that the depression state indicates depression onset,initiate closed-loop therapy by one or more electrodes.
 15. The systemof claim 12, wherein the instructions are further executable by theprocessor to adjust the first weight based on a state of currentphysical activity of the patient relating to a current level of exerciseof the patient.
 16. The system of claim 15, wherein the state ofactivity includes one or more of an awake state, a resting state, asleeping state, or an exercise state.
 17. The system of claim 12,wherein the first body parameter value is skin temperature, skinconductance, heart rate, change in heart rate, blood oxygen saturation,acceleration, respiration rate, or a value determined as a combinationthereof.
 18. A non-transitory computer-readable medium comprisinginstructions executable by a processor to: determine a firstdepression-indicative value based on a first body parameter value for apatient and a second depression-indicative value based on a second bodyparameter value for the patient; determine a first weight and a secondweight; determine a depression detection value based in part on thefirst weight and the first depression-indicative value and based in parton the second weight and the second weight and the seconddepression-indicative value; and determine a depression state based atleast in part on a comparison of the depression detection value to afirst threshold value and a second threshold value different from thefirst threshold value, the first threshold value associated with anonset of a depression episode, the second threshold value associatedwith an offset of the depression episode.
 19. The non-transitorycomputer-readable medium of claim 18, further comprising instructionsexecutable by the processor to perform at least one of: adjusting thefirst weight based on a current state of physical activity of thepatient relating to a current level of exercise of the patient; oradjusting at least one of a first set of rules used to determine thefirst weight or a second set of rules used to determine the secondweight based on sensor data for the patient that indicates depressiononsets and offsets.
 20. The non-transitory computer-readable medium ofclaim 18, further comprising instructions executable by the processorto, in response to determining that the depression state indicatesdepression onset, initiate closed-loop therapy by one or moreelectrodes.